
"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import PoolFormerImageProcessor



class 			lowercase					( unittest.TestCase		):






			def __init__(self  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Dict=7      ,SCREAMING_SNAKE_CASE_  :	List[Any]=3      ,SCREAMING_SNAKE_CASE_  :	Dict=30      ,SCREAMING_SNAKE_CASE_  :	Tuple=400      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=True      ,SCREAMING_SNAKE_CASE_  :	Dict=None      ,SCREAMING_SNAKE_CASE_  :	str=0.9      ,SCREAMING_SNAKE_CASE_  :	int=None      ,SCREAMING_SNAKE_CASE_  :	Tuple=True      ,SCREAMING_SNAKE_CASE_  :	int=[0.5, 0.5, 0.5]      ,SCREAMING_SNAKE_CASE_  :	Tuple=[0.5, 0.5, 0.5]      ,)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  size if size is not None else {'''shortest_edge''': 30}
				lowerCAmelCase												=  crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
				lowerCAmelCase												=  parent
				lowerCAmelCase												=  batch_size
				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  min_resolution
				lowerCAmelCase												=  max_resolution
				lowerCAmelCase												=  do_resize_and_center_crop
				lowerCAmelCase												=  size
				lowerCAmelCase												=  crop_pct
				lowerCAmelCase												=  crop_size
				lowerCAmelCase												=  do_normalize
				lowerCAmelCase												=  image_mean
				lowerCAmelCase												=  image_std






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	List[str]:





				"""simple docstring"""


				return {
				    "size": self.size,
				    "do_resize_and_center_crop": self.do_resize_and_center_crop,
				    "crop_pct": self.crop_pct,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}





@require_torch
@require_vision
class 			lowercase					( lowercase__   ,unittest.TestCase		):
			lowercase        =     PoolFormerImageProcessor if is_vision_available() else None






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  PoolFormerImageProcessingTester(self			)






			@property
			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()






			def 					UpperCAmelCase							(self  :	Any			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_resize_and_center_crop'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''size'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''crop_pct'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''do_normalize'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''image_mean'''			)			)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,'''image_std'''			)			)






			def 					UpperCAmelCase							(self  :	int			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class.from_dict(self.image_processor_dict			)
				self.assertEqual(image_processor.size      ,{'''shortest_edge''': 30}			)
				self.assertEqual(image_processor.crop_size      ,{'''height''': 30, '''width''': 30}			)

				lowerCAmelCase												=  self.image_processing_class.from_dict(self.image_processor_dict      ,size=42      ,crop_size=84			)
				self.assertEqual(image_processor.size      ,{'''shortest_edge''': 42}			)
				self.assertEqual(image_processor.crop_size      ,{'''height''': 84, '''width''': 84}			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				pass






			def 					UpperCAmelCase							(self  :	str			)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				# create random PIL images
				lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_			)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,Image.Image			)

				# Test not batched input
				lowerCAmelCase												=  image_processing(image_inputs[0]      ,return_tensors='''pt'''			).pixel_values

				self.assertEqual(
				    encoded_images.shape      ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )      ,)

				# Test batched
				lowerCAmelCase												=  image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )      ,)






			def 					UpperCAmelCase							(self  :	int			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				# create random numpy tensors
				lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      ,numpify=SCREAMING_SNAKE_CASE_			)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,np.ndarray			)

				# Test not batched input
				lowerCAmelCase												=  image_processing(image_inputs[0]      ,return_tensors='''pt'''			).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )      ,)

				# Test batched
				lowerCAmelCase												=  image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )      ,)






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.image_processing_class(**self.image_processor_dict			)
				# create random PyTorch tensors
				lowerCAmelCase												=  prepare_image_inputs(self.image_processor_tester      ,equal_resolution=SCREAMING_SNAKE_CASE_      ,torchify=SCREAMING_SNAKE_CASE_			)
				for image in image_inputs:
					self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,torch.Tensor			)

				# Test not batched input
				lowerCAmelCase												=  image_processing(image_inputs[0]      ,return_tensors='''pt'''			).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,(
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )      ,)

				# Test batched
				lowerCAmelCase												=  image_processing(SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''			).pixel_values
				self.assertEqual(
				    encoded_images.shape      ,(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )      ,)




"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def __magic_name__	(				_lowerCamelCase:		Optional[Any]			)					->						Dict:

	'''simple docstring'''




	def wrapper(*_lowerCamelCase:		Any,					**_lowerCamelCase:		Union[str, Any]			):
		lowerCAmelCase												=  timeit.default_timer()
		lowerCAmelCase												=  func(*_lowerCamelCase,					**_lowerCamelCase			)
		lowerCAmelCase												=  timeit.default_timer() - starttime
		return delta

	lowerCAmelCase												=  func.__name__

	return wrapper




def __magic_name__	(				_lowerCamelCase:		dict,					_lowerCamelCase:		List[Any]=100,					_lowerCamelCase:		int=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  []
	lowerCAmelCase												=  seq_shapes or {}
	for i in range(_lowerCamelCase			):
		lowerCAmelCase												=  {}
		for col_id, (k, v) in enumerate(features.items()			):
			if isinstance(_lowerCamelCase,					_ArrayXD			):
				lowerCAmelCase												=  np.random.rand(*v.shape			).astype(v.dtype			)
			elif isinstance(_lowerCamelCase,					datasets.Value			):
				if v.dtype == "string":
					lowerCAmelCase												=  '''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCAmelCase												=  np.random.randint(10,					size=1			).astype(v.dtype			).item()
			elif isinstance(_lowerCamelCase,					datasets.Sequence			):
				while isinstance(_lowerCamelCase,					datasets.Sequence			):
					lowerCAmelCase												=  v.feature
				lowerCAmelCase												=  seq_shapes[k]
				lowerCAmelCase												=  np.random.rand(*_lowerCamelCase			).astype(v.dtype			)
			lowerCAmelCase												=  data

		dummy_data.append((i, example)			)

	return dummy_data




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		Tuple,					_lowerCamelCase:		Union[str, Any]=100,					_lowerCamelCase:		List[Any]=None			)					->						List[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  generate_examples(_lowerCamelCase,					num_examples=_lowerCamelCase,					seq_shapes=_lowerCamelCase			)

	with ArrowWriter(features=_lowerCamelCase,					path=_lowerCamelCase			) as writer:
		for key, record in dummy_data:
			lowerCAmelCase												=  features.encode_example(_lowerCamelCase			)
			writer.write(_lowerCamelCase			)

		lowerCAmelCase							,       lowerCAmelCase												=  writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""			)

	lowerCAmelCase												=  datasets.Dataset.from_file(filename=_lowerCamelCase,					info=datasets.DatasetInfo(features=_lowerCamelCase			)			)

	return dataset


